Whisper-finetune_all
This model is a fine-tuned version of openai/whisper-large-v2 on the Amitabha_all dataset.
It achieves the following results on the evaluation set:
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss |
Epoch |
Step |
Validation Loss |
Cer |
| 0.1067 |
2.5253 |
1000 |
0.0800 |
11.4694 |
| 0.0133 |
5.0505 |
2000 |
0.0102 |
3.3448 |
| 0.0017 |
7.5758 |
3000 |
0.0014 |
0.3232 |
| 0.0002 |
10.1010 |
4000 |
0.0003 |
0.2260 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
